Pathogenesis of Anti-integrin αIIb-mediated Fetal and Neonatal Alloimmune Thrombocytopenia: Establishment of Novel Murine Models in αIIb Deficient and Human αIIb Transgenic Mice
Bibliographic record
Abstract
Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a life threatening bleeding disorder caused by maternal antibodies targeting fetal/neonatal platelets. The incidence is approximately 1:1000 live births, but the frequency of FNAIT associated miscarriage has not been adequately investigated. FNAIT targeting the β3 subunit of integrin αIIbβ3 occurs in approximately 85% of reported cases, whereas αIIb-mediated-FNAIT only accounts for approximately 5% of reported cases although frequencies of polymorphisms in αIIb and β3 polypeptides are similar in the human population. It is unknown whether immune responses to these two subunits differ and currently no animal model exists to address this question. We hypothesize that αIIb-mediated-FNAIT causes a severe form of FNAIT (e.g. miscarriage), which may account for this discrepancy. Here, two new murine models of αIIb-mediated-FNAIT were established. Severe FNAIT (e.g. miscarriage and intracranial hemorrhage) is frequently observed, and we conclude that the severity of anti-αIIb-FNAIT may mask the clinically reported incidence of disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".